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Image-based meshing

Image-based meshing is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Image-based meshing rather than just read about it. In short: Image-based meshing is the automated process of creating computer models for computational fluid dynamics (CFD) and finite element analysis (FEA) from 3D image data (such as magnetic resonance imaging (MRI), computed tomography (CT) or microtomography). Although a wide range of mesh generation techniques are currently available, these were usually developed to generate models from computer-aided design (CAD), and th…

Key takeaways

  • Image-based meshing belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Image-based meshing to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Image-based meshing from memory before moving on to harder problems.

Reference excerpt

Image-based meshing is the automated process of creating computer models for computational fluid dynamics (CFD) and finite element analysis (FEA) from 3D image data (such as magnetic resonance imaging (MRI), computed tomography (CT) or microtomography). Although a wide range of mesh generation techniques are currently available, these were usually developed to generate models from computer-aided design (CAD), and therefore have difficulties meshing from 3D imaging data.

Mesh generation from 3D imaging data Meshing from 3D imaging data presents a number of challenges but also unique opportunities for presenting a more realistic and accurate geometrical description of the computational domain. There are generally two ways of meshing from 3D imaging data:

CAD-based approach The majority of approaches used to date still follow the traditional CAD route by using an intermediary step of surface reconstruction which is then followed by a traditional CAD-based meshing algorithm. CAD-based approaches use the scan data to define the surface of the domain and then create elements within this defined boundary. Although reasonably robust algorithms are now available, these techniques are often time consuming, and virtually intractable for the complex topologies typical of image data. They also do not easily allow for more than one domain to be meshed, as multiple surfaces are often non-conforming with gaps or overlaps at interfaces where one or more structures meet.

Image-based approach This approach is the more direct way as it combines the geometric detection and mesh creation stages in one process which offers a more robust and accurate result than meshing from surface data. Voxel conversion technique providing meshes with brick elements and with tetrahedral elements have been proposed. Another approach generates 3D tetrahedral or tetrahedral elements throughout the volume of the domain, thus creating the mesh directly with conforming multipart surfaces.

Generating a model The steps involved in the generation of models based on 3D imaging data are:

Scan and image processing An extensive range of image processing tools can be used to generate highly accurate models based on data from 3D imaging modalities, e.g. MRI, CT, MicroCT (XMT), and Ultrasound. Features of particular interest include:

Segmentation tools (e.g. thresholding, floodfill, level set methods, etc.) Filters and smoothing tools (e.g. volume- and topology-preserving smoothing and noise reduction/artefact removing).

Volume and surface mesh generation The image-based meshing technique allows the straightforward generation of meshes out of segmented 3D data. Features of particular interest include:

Multi-part meshing (mesh any number of structures simultaneously) Mapping functions to apply material properties based on signal strength (e.g. Young's modulus to Hounsfield scale) Smoothing of meshes (e.g. topological preservation of data to ensure preservation of connectivity, and volume neutral smoothing to prevent shrinkage of convex hulls) Export to FEA and CFD codes for analysis (e.g. node sets, shell elements, material properties, contact surfaces, boundary layers, inlets/outlets)

Typical use Biomechanics and design of medical and dental implants Food science Forensic science Materials science (composites and foams) Nondestructive testing (NDT) Paleontology and functional morphology Reverse engineering Soil science Petrophysics

See also Image segmentation

References

External links Computing-Objects commercial C++ libraries for mesh generation & FEM computation ScanIP commercial image-based meshing software: www.simpleware.com Mimics 3D image-based engineering software for FEA and CFD on anatomical data: Mimics website Archived 2011-02-12 at the Wayback Machine Google group on image-based modelling: [1] Avizo Software's 3D image-based meshing tools for CFD and FEA iso2mesh: a free 3D surface and volumetric mesh generator for matlab/octave [2] OOF3D, object oriented finite element analysis from the NIST VGSTUDIO MAX, Commercial CT analysis software for industry. They offer an add-on module for FEM meshing.

Worked examples

Example 1 — a first encounter with Image-based meshing

Start with the simplest possible case. Write down what Image-based meshing claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Image-based meshing before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Image-based meshing ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Image-based meshing

In research
Image-based meshing appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Image-based meshing in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Image-based meshing is common in secondary-school and first-year university syllabi. It links to neighbouring topics 3D computer graphics, Computer graphics algorithms, Mesh generation, so understanding it makes those chapters shorter.
In everyday life
Look for Image-based meshing outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Image-based meshing in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Image-based meshing means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Image-based meshing out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Image-based meshing in simple terms?

Image-based meshing is the automated process of creating computer models for computational fluid dynamics (CFD) and finite element analysis (FEA) from 3D image data (such as magnetic resonance imaging (MRI), computed tomography (CT) or microtomography). Although a wide range of mesh generation tech…

Why does Image-based meshing matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Image-based meshing?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Image-based meshing.

Tags

  • 3D computer graphics
  • Computer graphics algorithms
  • Mesh generation

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